AIMC Topic: Humans

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Application of machine learning for identification of key exposure predictors for heavy metal accumulation in hair of traffic police officers in Tehran.

The Science of the total environment
In order to determine variability and measure the major exposure factors affecting the levels of hazardous metals (such as Fe, Mn, Ni, Pb, As, Cr, and Cu) in the scalp hair of Tehran traffic police personnel, an advanced statistical method is used. T...

The micro- and nanoplastics exposome feedback loop drives synergistic air-lung toxicity.

The Science of the total environment
Airborne micro- and nanoplastics (MNPs) are now recognized as persistent components of the atmospheric exposome. While their presence is established, their toxicological role remains incompletely understood. In this perspective, we propose the MNP Ex...

A Comparative Study of Deep Learning and Classical Modeling Approaches for Protein-Ligand Binding Pose and Affinity Prediction in Coronavirus Main Proteases.

Journal of chemical information and modeling
The accurate prediction of protein-ligand binding poses and affinities is central to structure-based drug design. In this study, we first benchmarked three distinct pose generation strategies for data sets from the ASAP Antiviral Challenge 2025: mole...

A neuromorphic robotic electronic skin with active pain and injury perception.

Proceedings of the National Academy of Sciences of the United States of America
Humanoid robots with advanced sensory capabilities are increasingly demanded for empathetic, close-contact interactions with humans. Electronic skin (E-skin) is a key enabling technology for such tactile perception. However, current E-skins are limit...

Multiplex mapping of protein-protein interaction interfaces.

Proceedings of the National Academy of Sciences of the United States of America
We describe peptide mapping through Split Antibiotic Resistance Complementation (SpARC-map), a method to identify the probable interface between two interacting proteins. Our method is based on in vivo affinity selection inside a bacterial host and u...

AI-generated biochemistry test item parameters in MST test conditions.

BMC medical education
BACKGROUND: This study investigated whether ChatGPT 4o could accurately estimate the difficulty of medical assessment items by comparing its predictions with empirically-derived parameters from multistage testing simulations.

Deep learning-based MRI model for predicting P53-mutated hepatocellular carcinoma.

BMC medical imaging
BACKGROUND: The P53-mutated Hepatocellular Carcinoma (HCC) is an aggressive variant associated with vascular endothelial growth factor (VEGF) overexpression and increased microvascular density. This study aimed to develop an MRI-based deep learning m...

Optimizing intervertebral disc cell metabolic phenotyping with machine learning and artificial neural networks.

Scientific reports
Biological phenotyping of cellular metabolism is essential for deciphering health and disease states. The Seahorse XF analyzer enables direct measurement of oxygen consumption rate (OCR) and extracellular acidification rate (ECAR), providing insight ...

Developing an AI-driven multimodal approach to visualising resilient team performance: joint attentional engagement with gaze and speech in simulated emergency scenarios.

BMJ open quality
INTRODUCTION: Healthcare team performance directly impacts the quality and safety of medical care. However, measuring the performance of teams is challenging and requires methodologies to investigate different contributing elements. This study propos...

AI-generated videos in medical education: systematic review.

BMJ open quality
BACKGROUND: Artificial intelligence (AI)-generated text to video is emerging in medical education, but its effectiveness, accuracy and safety remain uncertain. We aimed to synthesise empirical studies evaluating these tools in learner or patient educ...